How is it possible that deep neural networks are so easily fooled?
First up, those images (even the first few) aren't complete trash despite being junk to humans; they're actually finely tuned with various advanced techniques, including another neural network.


  The deep neural network is the pre-trained network modeled on AlexNet provided by Caffe. To evolve images, both the directly encoded and indirectly encoded images, we use the Sferes evolutionary framework. The entire code base to conduct the evolutionary experiments can be download [sic] here. The code for the images produced by gradient ascent is available here.


Images that are actually random junk were correctly recognized as nothing meaningful:


  In response to an unrecognizable image, the networks could have output a low confidence for each of the 1000 classes, instead of an extremely high confidence value for one of the classes. In fact, they do just that for randomly generated images (e.g. those in generation 0 of the evolutionary run)


The original goal of the researchers was to use the neural networks to automatically generate images that look like the real things (by getting the recognizer's feedback and trying to change the image to get a more confident result), but they ended up creating the above art. Notice how even in the static-like images there are little splotches - usually near the center - which, it's fair to say, are triggering the recognition.


  We were not trying to produce adversarial, unrecognizable images. Instead, we were trying to produce recognizable images, but these unrecognizable images emerged.


Evidently, these images had just the right distinguishing features to match what the AI looked for in pictures. The "paddle" image does have a paddle-like shape, the "bagel" is round and the right color, the "projector" image is a camera-lens-like thing, the "computer keyboard" is a bunch of rectangles (like the individual keys), and the "chainlink fence" legitimately looks like a chain-link fence to me.


  Figure 8. Evolving images to match DNN classes produces a tremendous diversity of images. Shown are images selected to showcase diversity from 5 evolutionary runs. The diversity suggests that the images are non-random, but that instead evolutions producing [sic] discriminative features of each target class.


Further reading: the original paper (large PDF)